Was ist das eigentlich? Cyberrisiken verständlich erklärt
Es wird viel über Cyberrisiken gesprochen. Oftmals fehlt aber das grundsätzliche Verständnis, was Cyberrisiken überhaupt sind. Ohne diese zu verstehen, lässt sich aber auch kein Versicherungsschutz gestalten.
Beinahe alle Aktivitäten des täglichen Lebens können heute über das Internet abgewickelt werden. Online-Shopping und Online-Banking sind im Alltag angekommen. Diese Entwicklung trifft längst nicht nur auf Privatleute, sondern auch auf Firmen zu. Das Schlagwort Industrie 4.0 verheißt bereits eine zunehmende Vernetzung diverser geschäftlicher Vorgänge über das Internet.
Anbieter von Cyberversicherungen für kleinere und mittelständische Unternehmen (KMU) haben Versicherungen die Erfahrung gemacht, dass trotz dieser eindeutigen Entwicklung Cyberrisiken immer noch unterschätzt werden, da sie als etwas Abstraktes wahrgenommen werden. Für KMU kann dies ein gefährlicher Trugschluss sein, da gerade hier Cyberattacken existenzbedrohende Ausmaße annehmen können. So wird noch häufig gefragt, was Cyberrisiken eigentlich sind. Diese Frage ist mehr als verständlich, denn ohne (Cyber-)Risiken bestünde auch kein Bedarf für eine (Cyber-)Versicherung.
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Exam Name : Designing and Implementing a Microsoft Azure AI Solution
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AI-102 test Format | AI-102 Course Contents | AI-102 Course Outline | AI-102 test Syllabus | AI-102 test Objectives
Exam Number: AI-102
Exam Name : Designing and Implementing a Microsoft Azure AI Solution
Exam TOPICS
The Cisco Customer Success Manager (DTCSM) v2.1 course gives you the confidence and competence to fulfill the Customer Success Manager (CSM) role successfully, helping your customers realize value from their solutions and achieve their business outcomes. The course offers experiential learning through practical exercises using situations based on real-life use cases and case studies. In this highly interactive course, you can practice and gain confidence in fulfilling core tasks using best-practice tools and methodologies while receiving feedback from the facilitator and your peers.
Candidates for the Azure AI Engineer Associate certification should have subject matter expertise building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
Azure AI Engineers work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this certification should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principle.
Plan and manage an Azure Cognitive Services solution
Implement Computer Vision solutions
Implement natural language processing solutions
Implement knowledge mining solutions
Implement conversational AI solutions
Plan and Manage an Azure Cognitive Services Solution (15-20%)
Select the appropriate Cognitive Services resource
select the appropriate cognitive service for a vision solution
select the appropriate cognitive service for a language analysis solution
select the appropriate cognitive Service for a decision support solution
select the appropriate cognitive service for a speech solution
Plan and configure security for a Cognitive Services solution
manage Cognitive Services account keys
manage authentication for a resource
secure Cognitive Services by using Azure Virtual Network
plan for a solution that meets responsible AI principles
Create a Cognitive Services resource
create a Cognitive Services resource
configure diagnostic logging for a Cognitive Services resource
manage Cognitive Services costs
monitor a cognitive service
implement a privacy policy in Cognitive Services
Plan and implement Cognitive Services containers
identify when to deploy to a container
containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics,
Speech, Form Recognizer)
deploy Cognitive Services Containers in Microsoft Azure
Implement Computer Vision Solutions (20-25%)
Analyze images by using the Computer Vision API
retrieve image descriptions and tags by using the Computer Vision API
identify landmarks and celebrities by using the Computer Vision API
detect brands in images by using the Computer Vision API
moderate content in images by using the Computer Vision API
generate thumbnails by using the Computer Vision API
Extract text from images
extract text from images or PDFs by using the Computer Vision service
extract information using pre-built models in Form Recognizer
build and optimize a custom model for Form Recognizer
Extract facial information from images
detect faces in an image by using the Face API
recognize faces in an image by using the Face API
analyze facial attributes by using the Face API
match similar faces by using the Face API
Implement image classification by using the Custom Vision service
label images by using the Computer Vision Portal
train a custom image classification model in the Custom Vision Portal
train a custom image classification model by using the SDK
manage model iterations
evaluate classification model metrics
publish a trained iteration of a model
export a model in an appropriate format for a specific target
consume a classification model from a client application
deploy image classification custom models to containers
Implement an object detection solution by using the Custom Vision service
label images with bounding boxes by using the Computer Vision Portal
train a custom object detection model by using the Custom Vision Portal
train a custom object detection model by using the SDK
manage model iterations
evaluate object detection model metrics
publish a trained iteration of a model
consume an object detection model from a client application
deploy custom object detection models to containers
Analyze video by using Video Indexer
process a video
extract insights from a video
moderate content in a video
customize the Brands model used by Video Indexer
customize the Language model used by Video Indexer by using the Custom Speech service
customize the Person model used by Video Indexer
extract insights from a live stream of video data
Implement Natural Language Processing Solutions (20-25%)
Analyze text by using the Text Analytics service
retrieve and process key phrases
retrieve and process entity information (people, places, urls, etc.)
retrieve and process sentiment
detect the language used in text
Manage speech by using the Speech service
implement text-to-speech
customize text-to-speech
implement speech-to-text
Strengthen
speech-to-text accuracy
Strengthen
text-to-speech accuracy
implement intent recognition
Translate language
translate text by using the Translator service
translate speech-to-speech by using the Speech service
translate speech-to-text by using the Speech service
Build an initial language model by using Language Understanding Service (LUIS)
create intents and entities based on a schema, and then add utterances
create complex hierarchical entities
o use this instead of roles
train and deploy a model
Iterate on and optimize a language model by using LUIS
implement phrase lists
implement a model as a feature (i.e. prebuilt entities)
manage punctuation and diacritics
implement active learning
monitor and correct data imbalances
implement patterns
Manage a LUIS model
manage collaborators
manage versioning
publish a model through the portal or in a container
export a LUIS package
deploy a LUIS package to a container
integrate Bot Framework (LUDown) to run outside of the LUIS portal
Implement Knowledge Mining Solutions (15-20%)
Implement a Cognitive Search solution
create data sources
define an index
create and run an indexer
query an index
configure an index to support autocomplete and autosuggest
boost results based on relevance
implement synonyms
Implement an enrichment pipeline
attach a Cognitive Services account to a skillset
select and include built-in skills for documents
implement custom skills and include them in a skillset
Implement a knowledge store
define file projections
define object projections
define table projections
query projections
Manage a Cognitive Search solution
provision Cognitive Search
configure security for Cognitive Search
configure scalability for Cognitive Search
Manage indexing
manage re-indexing
rebuild indexes
schedule indexing
monitor indexing
implement incremental indexing
manage concurrency
push data to an index
troubleshoot indexing for a pipeline
Implement Conversational AI Solutions (15-20%)
Create a knowledge base by using QnA Maker
create a QnA Maker service
create a knowledge base
import a knowledge base
train and test a knowledge base
publish a knowledge base
create a multi-turn conversation
add alternate phrasing
add chit-chat to a knowledge base
export a knowledge base
add active learning to a knowledge base
manage collaborators
Design and implement conversation flow
design conversation logic for a bot
create and evaluate *.chat file conversations by using the Bot Framework Emulator
choose an appropriate conversational model for a bot, including activity handlers and dialogs
Create a bot by using the Bot Framework SDK
use the Bot Framework SDK to create a bot from a template
implement activity handlers and dialogs
use Turn Context
test a bot using the Bot Framework Emulator
deploy a bot to Azure
Create a bot by using the Bot Framework Composer
implement dialogs
maintain state
implement logging for a bot conversation
implement prompts for user input
troubleshoot a conversational bot
test a bot
publish a bot
add language generation for a response
design and implement adaptive cards
Integrate Cognitive Services into a bot
integrate a QnA Maker service
integrate a LUIS service
integrate a Speech service
integrate Dispatch for multiple language models
manage keys in app settings file
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Warum sind Cyberrisiken so schwer greifbar?
Als mehr oder weniger neuartiges Phänomen stellen Cyberrisiken Unternehmen und Versicherer vor besondere Herausforderungen. Nicht nur die neuen Schadenszenarien sind abstrakter oder noch nicht bekannt. Häufig sind immaterielle Werte durch Cyberrisiken in Gefahr. Diese wertvollen Vermögensgegenstände sind schwer bewertbar.
Obwohl die Gefahr durchaus wahrgenommen wird, unterschätzen viele Firmen ihr eigenes Risiko. Dies liegt unter anderem auch an den Veröffentlichungen zu Cyberrisiken. In der Presse finden sich unzählige Berichte von Cyberattacken auf namhafte und große Unternehmen. Den Weg in die Presse finden eben nur die spektakulären Fälle. Die dort genannten Schadenszenarien werden dann für das eigene Unternehmen als unrealistisch eingestuft. Die für die KMU nicht minder gefährlichen Cyberattacken werden nur selten publiziert.
Aufgrund der fehlenden öffentlichen Meldungen von Sicherheitsvorfällen an Sicherheitsbehörden und wegen der fehlenden Presseberichte fällt es schwer, Fakten und Zahlen zur Risikolage zu erheben. Aber ohne diese Grundlage fällt es schwer, in entsprechende Sicherheitsmaßnahmen zu investieren.
Erklärungsleitfaden anhand eines Ursache-Wirkungs-Modells
Häufig nähert man sich dem Thema Cyberrisiko anlass- oder eventbezogen, also wenn sich neue Schadenszenarien wie die weltweite WannaCry-Attacke entwickeln. Häufig wird auch akteursgebunden beleuchtet, wer Angreifer oder Opfer sein kann. Dadurch begrenzt man sich bei dem Thema häufig zu sehr nur auf die Cyberkriminalität. Um dem Thema Cyberrisiko jedoch gerecht zu werden, müssen auch weitere Ursachen hinzugezogen werden.
Mit einer Kategorisierung kann das Thema ganzheitlich und nachvollziehbar strukturiert werden. Ebenso hilft eine solche Kategorisierung dabei, eine Abgrenzung vorzunehmen, für welche Gefahren Versicherungsschutz über eine etwaige Cyberversicherung besteht und für welche nicht.
Die Ursachen sind dabei die Risiken, während finanzielle bzw. nicht finanzielle Verluste die Wirkungen sind. Cyberrisiken werden demnach in zwei Hauptursachen eingeteilt. Auf der einen Seite sind die nicht kriminellen Ursachen und auf der anderen Seite die kriminellen Ursachen zu nennen. Beide Ursachen können dabei in drei Untergruppen unterteilt werden.
Nicht kriminelle Ursachen
Höhere Gewalt
Häufig hat man bei dem Thema Cyberrisiko nur die kriminellen Ursachen vor Augen. Aber auch höhere Gewalt kann zu einem empfindlichen Datenverlust führen oder zumindest die Verfügbarkeit von Daten einschränken, indem Rechenzentren durch Naturkatastrophen wie beispielsweise Überschwemmungen oder Erdbeben zerstört werden. Ebenso sind Stromausfälle denkbar.
Menschliches Versagen/Fehlverhalten
Als Cyberrisiken sind auch unbeabsichtigtes und menschliches Fehlverhalten denkbar. Hierunter könnte das versehentliche Veröffentlichen von sensiblen Informationen fallen. Möglich sind eine falsche Adressierung, Wahl einer falschen Faxnummer oder das Hochladen sensibler Daten auf einen öffentlichen Bereich der Homepage.
Technisches Versagen
Auch Hardwaredefekte können zu einem herben Datenverlust führen. Neben einem Überhitzen von Rechnern sind Kurzschlüsse in Systemtechnik oder sogenannte Headcrashes von Festplatten denkbare Szenarien.
Kriminelle Ursachen
Hackerangriffe
Hackerangriffe oder Cyberattacken sind in der Regel die Szenarien, die die Presse dominieren. Häufig wird von spektakulären Datendiebstählen auf große Firmen oder von weltweiten Angriffen mit sogenannten Kryptotrojanern berichtet. Opfer kann am Ende aber jeder werden. Ziele, Methoden und auch das Interesse sind vielfältig. Neben dem finanziellen Interesse können Hackerangriffe auch zur Spionage oder Sabotage eingesetzt werden. Mögliche Hackermethoden sind unter anderem: Social Engineering, Trojaner, DoS-Attacken oder Viren.
Physischer Angriff
Die Zielsetzung eines physischen Angriffs ist ähnlich dem eines Hackerangriffs. Dabei wird nicht auf die Tools eines Hackerangriffs zurückgegriffen, sondern durch das physische Eindringen in Unternehmensgebäude das Ziel erreicht. Häufig sind es Mitarbeiter, die vertrauliche Informationen stehlen, da sie bereits den notwendigen Zugang zu den Daten besitzen.
Erpressung
Obwohl die Erpressung aufgrund der eingesetzten Methoden auch als Hackerangriff gewertet werden könnte, ergibt eine Differenzierung Sinn. Erpressungsfälle durch Kryptotrojaner sind eines der häufigsten Schadenszenarien für kleinere und mittelständische Unternehmen. Außerdem sind auch Erpressungsfälle denkbar, bei denen sensible Daten gestohlen wurden und ein Lösegeld gefordert wird, damit sie nicht veröffentlicht oder weiterverkauft werden.
Ihre Cyberversicherung sollte zumindet folgende Schäden abdecken:
Cyber-Kosten:
- Soforthilfe und Forensik-Kosten (Kosten der Ursachenermittlung, Benachrichtigungskosten und Callcenter-Leistung)
- Krisenkommunikation / PR-Maßnahmen
- Systemverbesserungen nach einer Cyber-Attacke
- Aufwendungen vor Eintritt des Versicherungsfalls
Cyber-Drittschäden (Haftpflicht):
- Befriedigung oder Abwehr von Ansprüchen Dritter
- Rechtswidrige elektronische Kommunikation
- Ansprüche der E-Payment-Serviceprovider
- Vertragsstrafe wegen der Verletzung von Geheimhaltungspflichten und Datenschutzvereinbarungen
- Vertragliche Schadenersatzansprüche
- Vertragliche Haftpflicht bei Datenverarbeitung durch Dritte
- Rechtsverteidigungskosten
Cyber-Eigenschäden:
- Betriebsunterbrechung
- Betriebsunterbrechung durch Ausfall von Dienstleister (optional)
- Mehrkosten
- Wiederherstellung von Daten (auch Entfernen der Schadsoftware)
- Cyber-Diebstahl: elektronischer Zahlungsverkehr, fehlerhafter Versand von Waren, Telefon-Mehrkosten/erhöhte Nutzungsentgelte
- Cyber-Erpressung
- Entschädigung mit Strafcharakter/Bußgeld
- Ersatz-IT-Hardware
- Cyber-Betrug